This article gives a brief summary of major approaches in dual-purpose computerized adaptive testing (CAT) in which the test is tailored interactively to both an examinee's overall ability level, , and attribute mastery level, . It also proposes an information product approach whose connections to the current methods are revealed. An updated comprehensive empirical study demonstrated that the information product approach not only can offer a unified framework to connect all other approaches but also can mitigate the weighting issue in the dual-information approach.

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For aircrafts equipped with BeiDou Navigation Satellite System/Strapdown Inertial Navigation System integrated navigation system, BeiDou Navigation Satellite System information can be used to achieve autonomous alignment. However, due to the complex polar environment and multipath effect, BeiDou Navigation Satellite System measurement noise often exhibits a non-Gaussian distribution that will severely degrade the estimation accuracy of standard Kalman filter. To address this problem, a new polar alignment algorithm based on the Huber estimation filter is proposed in this article. Considering the special geographical conditions in the polar regions, the dynamic model and the measurement model of BeiDou Navigation Satellite System/Strapdown Inertial Navigation System integrated alignment system in the grid frame are derived in this article. The BeiDou Navigation Satellite System measurement noise characteristics in the polar regions are analyzed and heavy-tailed characteristics are simulated, respectively. Since the estimation accuracy of standard Kalman filter can be severely degraded under non-Gaussian noise, a Kalman filter based on the Huber estimation is designed combining grid navigation system and generalized maximum likelihood estimation. The simulation and experiment results demonstrate that the proposed algorithm has better robustness under non-Gaussian noise, and it is effective in the polar regions. By employing the proposed algorithm, the rapidity and accuracy of the alignment process can be improved.

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In order to explore the species composition and invasion risk of dinoflagellate resting cysts in the sediments of ballast tanks of foreign ships, we identified and analyzed the species of living dinoflagellate cysts in the sediments of five foreign ships ballast tanks in Jiangyin Port based on the morphological characteristics and molecular biology information of dormant cysts and germinating cells. A total of 29 species of dinoflagellate cysts (excluding three unkonwn species), belonging to Gonyaulacoid group, Calcoidinellid group, Gymnodinioid group, Diplopsalid group and Protoperidinioid group, were recorded in ballast sediments. The density of living cysts ranged from 0.73 to 44.3 cysts/g DW. Cysts of toxic and harmful dinoflagellate Alexandrium tamarense species complex, Protoceratium reticulatum, and Gonyaulax spinifera were found in five ships. In addition, a variety of unrecorded dinoflagellate cyst types in Chinese coasts have been found, one of which was identified as Alexandrium insuetum. Germination and incubation experiments showed that it can proliferate in coastal waters of China. Therefore, Alexandrium insuetum has potential invasion risk and possibility of bloom in coastal China.

To solve the problem of Kalman filter (KF) performance degradation in unmanned aerial vehicle (UAV) applications, a novel interactive robust filter algorithm for GNSS/SINS integrated navigation is proposed in this paper. The strong tracking Kalman filter (STKF) is robust to uncertain system noise but is ineffective to abnormal measurement information. Based on the same performance index function with STKF, a measurement noise covariance matrix adaptive Kalman filter algorithm (MAKF) is presented, but it is ineffective under uncertain system noise. Furthermore, the interactive robust filter algorithm based on STKF and MAKF (IF-STKF-MAKF) is proposed, given the complementary characteristics of the above two filter algorithms. The STKF and MAKF operate in parallel based on the same system model. The filter probability of each filter is updated according to the likelihood function to perform output fusion and input interaction. The simulation and experiment results demonstrate that the IF-STKF-MAKF is effective and can achieve high estimation accuracy under both system noise anomalies and measurement information anomalies. In the vehicle experiment, the position accuracy of the proposed IF-STKF-MAKF method has been improved by more than 30% compared with KF, STKF, and MAKF. This method can also be extended to land vehicles, mobile robots, etc.

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In order to improve the continuity and smoothness of transpolar flight and optimize autonomous navigation performance, a SINS/SRS/CNS (strapdown inertial navigation system/spectral redshift navigation system/celestial navigation system) multi-information fusion global autonomous navigation method based on parameter conversion was studied for this article. The global autonomous navigation scheme based on multi-information fusion was designed. The principle of spectral redshift navigation was studied. On this basis, the system equations of the SINS/SRS/CNS multi-information fusion global autonomous navigation system were established in the middle–low latitudes and high latitudes. Furthermore, the navigation and filter parameter conversion relationships between the geographic navigation coordinate frame and the grid navigation coordinate frame were derived. The simulation and experiment verified that the SINS/SRS/CNS multi-information fusion global autonomous navigation method with parameter conversion can effectively improve the accuracy and smoothness and realize non-oscillation switching in transpolar navigation. In the vehicle experiment, the proposed algorithm improved the horizontal position accuracy by more than 29% compared with the multi-information fusion global autonomous navigation method without filter parameter conversion.